& Methods Data from 418 sample plots were used to adjust generic models for forest types and specific models for 15 species. Regression assumptions, modelling efficiency, lack of fitness, goodness of fit and comparison between species-specific and generic models were assessed by analytical methods. & Results Logarithmic models presented the best results of adjustment and evenness of residual variance. Lack of fit F test showed acceptable adjust quality for nearly all speciesspecific and generic models; R 2 adj * and modelling efficiency measure presented values close to 1 for all fitted models; model identity F test showed differences between specific and generic models in some cases. & Conclusion Since regression assumptions were satisfied and because of their quality of fit, the fitted models compose useful tools for predicting total stem volume (with bark) for forest remnants in southern Brazil. Stratification of datasets by forest type for model fitting showed to be necessary, but, commonly, generic models for forest types produced estimates not less reliable than species-specific models.

A key issue in large-area inventories is defining a suitable sampling design and the effort required to obtain reliable estimates of species richness and forest attributes, especially in species-diverse forests. To address this issue, data from 418 systematically distributed 0.4 ha plots were collected. Estimators of nonparametric species richness were employed to assess the floristic representativeness of data collected in three forest types in the Brazilian Atlantic Forest. The sampling sufficiency of forest attributes was evaluated as a function of sample size. Altogether, 831 tree/shrub species were recorded. The data acquired through the systematic sampling design were representative of both species richness and basal area. The confidence intervals' length would not substantially decrease by using more than 70 % of the reference sample (n = 364), thereby reaching a length of ~5 % of the sample mean. Nevertheless, reliable estimates of species richness for diverse forests demand a thorough sampling approach far more exacting so as to achieve acceptable population estimates of forest attributes. Though the study area is regarded as a biodiversity hotspot, the forest stands showed diminished species richness, basal area, stem volume and biomass when compared to old-growth stands. As regards species richness, the data provided evidence of contrasting great γ-diversity (at the forest type level) and small α-diversity (at the forest stand level). Amongst anthropic impacts, illegal logging and extensive cattle grazing within stands are undoubtedly key factors that threaten forest conservation in the study area.

Forest inventory estimates of tree volume for large areas are typically calculated by adding the model predictions of volumes for individual trees at the plot level, calculating the mean over plots, and expressing the result on a per unit area basis. The uncertainty in the model predictions is generally ignored, with the result that the precision of the large-area volume estimate is optimistic. The primary study objective was to assess the performance of a Monte Carlo based approach for estimating model prediction error that had been developed for boreal and temperate forest applications when used for a subtropical forest application. Monte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty, the nonlinear nature of the models, and heteroskedasticity. A related objective was to estimate the effects of model prediction uncertainty due to residual and parameter uncertainty on the large-area volume estimates for the Brazilian state of Santa Catarina. The primary conclusions were fourfold. First, the methodological approach worked well. Second, the effects of model residual and parameter uncertainty on large-area estimates of mean volume per unit area were negligible for the models and calibration datasets used for the study. Third, for the models currently in use in Santa Catarina, the effects of model residual and parameter uncertainty may be ignored when calculating large-area estimates of mean volume per unit area. Fourth, differences were negligible between estimates of the mean and standard error obtained using a single, nonspecific volume model and estimates obtained using both forest-type models and species-specific/species-group models.

The majority of Atlantic Forest fragments in Southern Brazil are second-growth forests dominated by fast-growing species with considerable market-value timber. Nevertheless, volume prediction models are scarce, especially to estimate tree total volume (i.e., stem plus branches). This study approached the issue through the following aims: to fit and select stem and total volume models (generic and species-specific) using data from 288 harvested trees in a management operation, and to fit generic and species-specific bark factors. The power model embedding diameter at breast height (D) and tree stem or total height (H) presented the greatest prediction strength for both stem and total tree volume. Models including only D to predict total tree volume were similar to double-entry models regarding goodness-of-fit. Therefore, they may be useful in the context of subtropical closed-canopy forests, where the difficulty and uncertainty in H measurements are not trivial. Species-specific models fitted for Miconia cinnamomifolia (DC) Naudin. and Hyeronima alchorneoides Allemão surpassed generic models only for the former species. Nevertheless, the prediction improvement should offset the eventual extra efforts implied in the collection of reliable samples of these species. Finally, bark factors stood as a satisfactory tool for inside bark mean volume estimation.

Th e eff ect of environmental variables on the plant species distribution in the Atlantic Forest raises the interest of researchers, but few studies, mainly at local scales, analyzed the infl uence of spatial factors on the environment and species distribution. Linear models and spatial correlograms were used to evaluate whether environment and space may predict variations in species composition of trees and shrubs in a Subtropical Rainforest remnant. Th e study area was divided in 25 plots of 400 m², systematically distributed, where individuals with DBH ≥ 5 cm were measured. Elevation and slope were the most important predictors of the fl oristic variations, but space, generally neglected in researches on South American's vegetation, had a relevant infl uence, as a structural component, in terms of environmental variables, and as intrinsic biological component, here probably as result of constraints in species dispersion. Although the origin of great part of the variation in species composition remained unknown, which is common in studies on vegetation, results indicated, for the fi rst time, the complex role of spatial and environmental variables in the composition of trees and shrub species in a Subtropical Rainforest of Brazil.

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Wood density ( ρ ) is a trait involved in forest biomass estimates, forest ecology, prediction of stand stability, wood science, and engineering. Regardless of its importance, data on ρ are scarce for a substantial number of species of the vast Atlantic Forest phytogeographic domain. Given that, the present paper describes a dataset composed of three data tables: (i) determinations of ρ (kg m−3) for 153 species growing in three forest types within the subtropical Atlantic Forest, based on wood samples collected throughout the state of Santa Catarina, southern Brazil; (ii) a list of 719 tree/shrub species observed by a state-level forest inventory and a ρ value assigned to each one of them based on local determinations and on a global database; (iii) the means and standard deviations of ρ for 477 permanent sample plots located in the subtropical Atlantic Forest, covering ∼95,000 km2. The mean ρ over the 153 sampled species is 538.6 kg m−3 (standard deviation = 120.5 kg m−3), and the mean ρ per sample plot, considering the three forest types, is 525.0 kg m−3 (standard error = 1.8 kg m−3). The described dataset has potential to underpin studies on forest biomass, forest ecology, alternative uses of timber resources, as well as to enlarge the coverage of global datasets.